Open AccessThe Real-Time Mesoscale Analysis at NOAA’s National Centers for Environmental Prediction: Current Status and Development
Author(s) -
Manuel Pondeca,
Geoffrey S. Manikin,
Geoff DiMego,
Stanley G. Benjamin,
David Parrish,
Robert James Purser,
Wan Shu Wu,
John D. Horel,
David T. Myrick,
Lin Ying,
Robert M. Aune,
Daniel Keyser,
Brad Colman,
Greg Mann,
Jamie Vavra
Publication year - 2011
Publication title -
weather and forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.393
H-Index - 106
eISSN - 1520-0434
pISSN - 0882-8156
DOI - 10.1175/waf-d-10-05037.1
Subject(s) - geostationary operational environmental satellite , environmental science , meteorology , mesoscale meteorology , data assimilation , geostationary orbit , numerical weather prediction , terrain , satellite , weather research and forecasting model , precipitation , climatology , geography , geology , cartography , aerospace engineering , engineering
In 2006, the National Centers for Environmental Prediction (NCEP) implemented the Real-Time Mesoscale Analysis (RTMA) in collaboration with the Earth System Research Laboratory and the National Environmental, Satellite, and Data Information Service (NESDIS). In this work, a description of the RTMA applied to the 5-km resolution conterminous U.S. grid of the National Digital Forecast Database is given. Its two-dimensional variational data assimilation (2DVAR) component used to analyze near-surface observations is described in detail, and a brief discussion of the remapping of the NCEP stage II quantitative precipitation amount and NESDIS Geostationary Operational Environmental Satellite (GOES) sounder effective cloud amount to the 5-km grid is offered. Terrain-following background error covariances are used with the 2DVAR approach, which produces gridded fields of 2-m temperature, 2-m specific humidity, 2-m dewpoint, 10-m U and V wind components, and surface pressure. The estimate of the analysis uncertainty via the Lanczos method is briefly described. The strength of the 2DVAR is illustrated by (i) its ability to analyze a June 2007 cold temperature pool over the Washington, D.C., area; (ii) its fairly good analysis of a December 2008 mid-Atlantic region high-wind event that started from a very weak first guess; and (iii) its successful recovery of the finescale moisture features in a January 2010 case study over southern California. According to a cross-validation analysis for a 15-day period during November 2009, root-mean-square error improvements over the first guess range from 16% for wind speed to 45% for specific humidity.